Overview

Dataset statistics

Number of variables24
Number of observations20000
Missing cells48
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 MiB
Average record size in memory200.0 B

Variable types

Numeric12
Categorical12

Alerts

Seat comfort is highly overall correlated with Food and drinkHigh correlation
Departure/Arrival time convenient is highly overall correlated with Food and drink and 1 other fieldsHigh correlation
Food and drink is highly overall correlated with Seat comfort and 2 other fieldsHigh correlation
Gate location is highly overall correlated with Departure/Arrival time convenient and 1 other fieldsHigh correlation
Inflight wifi service is highly overall correlated with Ease of Online booking and 1 other fieldsHigh correlation
Inflight entertainment is highly overall correlated with satisfaction_v2High correlation
Departure Delay in Minutes is highly overall correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly overall correlated with Departure Delay in MinutesHigh correlation
satisfaction_v2 is highly overall correlated with Inflight entertainmentHigh correlation
Type of Travel is highly overall correlated with ClassHigh correlation
Class is highly overall correlated with Type of TravelHigh correlation
Online support is highly overall correlated with Ease of Online booking and 1 other fieldsHigh correlation
Ease of Online booking is highly overall correlated with Inflight wifi service and 2 other fieldsHigh correlation
Baggage handling is highly overall correlated with CleanlinessHigh correlation
Cleanliness is highly overall correlated with Baggage handlingHigh correlation
Online boarding is highly overall correlated with Inflight wifi service and 2 other fieldsHigh correlation
id has unique valuesUnique
Seat comfort has 750 (3.8%) zerosZeros
Departure/Arrival time convenient has 1020 (5.1%) zerosZeros
Food and drink has 928 (4.6%) zerosZeros
Inflight entertainment has 438 (2.2%) zerosZeros
Departure Delay in Minutes has 11422 (57.1%) zerosZeros
Arrival Delay in Minutes has 11224 (56.1%) zerosZeros

Reproduction

Analysis started2023-08-14 05:35:49.194273
Analysis finished2023-08-14 05:36:18.702267
Duration29.51 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64401.368
Minimum3
Maximum129868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:18.833690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6349.6
Q131600
median64416.5
Q396830.5
95-th percentile122795.4
Maximum129868
Range129865
Interquartile range (IQR)65230.5

Descriptive statistics

Standard deviation37404.018
Coefficient of variation (CV)0.58079539
Kurtosis-1.2019883
Mean64401.368
Median Absolute Deviation (MAD)32613
Skewness0.014734277
Sum1.2880274 × 109
Variance1.3990605 × 109
MonotonicityNot monotonic
2023-08-14T08:36:19.116692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60672 1
 
< 0.1%
99096 1
 
< 0.1%
34742 1
 
< 0.1%
30271 1
 
< 0.1%
72392 1
 
< 0.1%
55626 1
 
< 0.1%
125397 1
 
< 0.1%
81639 1
 
< 0.1%
50385 1
 
< 0.1%
361 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
3 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
30 1
< 0.1%
31 1
< 0.1%
33 1
< 0.1%
ValueCountFrequency (%)
129868 1
< 0.1%
129861 1
< 0.1%
129859 1
< 0.1%
129857 1
< 0.1%
129851 1
< 0.1%
129828 1
< 0.1%
129819 1
< 0.1%
129814 1
< 0.1%
129793 1
< 0.1%
129782 1
< 0.1%

satisfaction_v2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
satisfied
10887 
neutral or dissatisfied
9113 

Length

Max length23
Median length9
Mean length15.3791
Min length9

Characters and Unicode

Total characters307582
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsatisfied
2nd rowneutral or dissatisfied
3rd rowneutral or dissatisfied
4th rowsatisfied
5th rowneutral or dissatisfied

Common Values

ValueCountFrequency (%)
satisfied 10887
54.4%
neutral or dissatisfied 9113
45.6%

Length

2023-08-14T08:36:19.354448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:19.529664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
satisfied 10887
28.5%
neutral 9113
23.8%
or 9113
23.8%
dissatisfied 9113
23.8%

Most occurring characters

ValueCountFrequency (%)
s 49113
16.0%
i 49113
16.0%
a 29113
9.5%
t 29113
9.5%
e 29113
9.5%
d 29113
9.5%
f 20000
6.5%
r 18226
 
5.9%
18226
 
5.9%
n 9113
 
3.0%
Other values (3) 27339
8.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 289356
94.1%
Space Separator 18226
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 49113
17.0%
i 49113
17.0%
a 29113
10.1%
t 29113
10.1%
e 29113
10.1%
d 29113
10.1%
f 20000
6.9%
r 18226
 
6.3%
n 9113
 
3.1%
u 9113
 
3.1%
Other values (2) 18226
 
6.3%
Space Separator
ValueCountFrequency (%)
18226
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 289356
94.1%
Common 18226
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 49113
17.0%
i 49113
17.0%
a 29113
10.1%
t 29113
10.1%
e 29113
10.1%
d 29113
10.1%
f 20000
6.9%
r 18226
 
6.3%
n 9113
 
3.1%
u 9113
 
3.1%
Other values (2) 18226
 
6.3%
Common
ValueCountFrequency (%)
18226
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 307582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 49113
16.0%
i 49113
16.0%
a 29113
9.5%
t 29113
9.5%
e 29113
9.5%
d 29113
9.5%
f 20000
6.5%
r 18226
 
5.9%
18226
 
5.9%
n 9113
 
3.0%
Other values (3) 27339
8.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
Female
10099 
Male
9901 

Length

Max length6
Median length6
Mean length5.0099
Min length4

Characters and Unicode

Total characters100198
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 10099
50.5%
Male 9901
49.5%

Length

2023-08-14T08:36:19.689215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:19.840727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
female 10099
50.5%
male 9901
49.5%

Most occurring characters

ValueCountFrequency (%)
e 30099
30.0%
a 20000
20.0%
l 20000
20.0%
F 10099
 
10.1%
m 10099
 
10.1%
M 9901
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80198
80.0%
Uppercase Letter 20000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30099
37.5%
a 20000
24.9%
l 20000
24.9%
m 10099
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 10099
50.5%
M 9901
49.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 100198
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30099
30.0%
a 20000
20.0%
l 20000
20.0%
F 10099
 
10.1%
m 10099
 
10.1%
M 9901
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30099
30.0%
a 20000
20.0%
l 20000
20.0%
F 10099
 
10.1%
m 10099
 
10.1%
M 9901
 
9.9%

Customer Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
Loyal Customer
16266 
disloyal Customer
3734 

Length

Max length17
Median length14
Mean length14.5601
Min length14

Characters and Unicode

Total characters291202
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoyal Customer
2nd rowLoyal Customer
3rd rowLoyal Customer
4th rowLoyal Customer
5th rowLoyal Customer

Common Values

ValueCountFrequency (%)
Loyal Customer 16266
81.3%
disloyal Customer 3734
 
18.7%

Length

2023-08-14T08:36:20.039618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:20.203855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
customer 20000
50.0%
loyal 16266
40.7%
disloyal 3734
 
9.3%

Most occurring characters

ValueCountFrequency (%)
o 40000
13.7%
l 23734
 
8.2%
s 23734
 
8.2%
y 20000
 
6.9%
a 20000
 
6.9%
20000
 
6.9%
C 20000
 
6.9%
u 20000
 
6.9%
t 20000
 
6.9%
m 20000
 
6.9%
Other values (5) 63734
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 234936
80.7%
Uppercase Letter 36266
 
12.5%
Space Separator 20000
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 40000
17.0%
l 23734
10.1%
s 23734
10.1%
y 20000
8.5%
a 20000
8.5%
u 20000
8.5%
t 20000
8.5%
m 20000
8.5%
e 20000
8.5%
r 20000
8.5%
Other values (2) 7468
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
C 20000
55.1%
L 16266
44.9%
Space Separator
ValueCountFrequency (%)
20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 271202
93.1%
Common 20000
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 40000
14.7%
l 23734
8.8%
s 23734
8.8%
y 20000
7.4%
a 20000
7.4%
C 20000
7.4%
u 20000
7.4%
t 20000
7.4%
m 20000
7.4%
e 20000
7.4%
Other values (4) 43734
16.1%
Common
ValueCountFrequency (%)
20000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 291202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 40000
13.7%
l 23734
 
8.2%
s 23734
 
8.2%
y 20000
 
6.9%
a 20000
 
6.9%
20000
 
6.9%
C 20000
 
6.9%
u 20000
 
6.9%
t 20000
 
6.9%
m 20000
 
6.9%
Other values (5) 63734
21.9%

Age
Real number (ℝ)

Distinct75
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.38335
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:20.394608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.130811
Coefficient of variation (CV)0.38419309
Kurtosis-0.71801681
Mean39.38335
Median Absolute Deviation (MAD)12
Skewness-0.0080533337
Sum787667
Variance228.94144
MonotonicityNot monotonic
2023-08-14T08:36:20.633678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 575
 
2.9%
25 527
 
2.6%
40 497
 
2.5%
41 490
 
2.5%
42 474
 
2.4%
44 471
 
2.4%
45 469
 
2.3%
37 461
 
2.3%
49 458
 
2.3%
26 456
 
2.3%
Other values (65) 15122
75.6%
ValueCountFrequency (%)
7 103
0.5%
8 137
0.7%
9 119
0.6%
10 142
0.7%
11 125
0.6%
12 109
0.5%
13 128
0.6%
14 138
0.7%
15 177
0.9%
16 210
1.1%
ValueCountFrequency (%)
85 4
 
< 0.1%
80 17
0.1%
79 8
 
< 0.1%
78 6
 
< 0.1%
77 15
0.1%
76 8
 
< 0.1%
75 16
0.1%
74 11
 
0.1%
73 8
 
< 0.1%
72 34
0.2%

Type of Travel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
Business travel
13839 
Personal Travel
6161 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters300000
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBusiness travel
2nd rowBusiness travel
3rd rowPersonal Travel
4th rowBusiness travel
5th rowPersonal Travel

Common Values

ValueCountFrequency (%)
Business travel 13839
69.2%
Personal Travel 6161
30.8%

Length

2023-08-14T08:36:20.865792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:21.116597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
travel 20000
50.0%
business 13839
34.6%
personal 6161
 
15.4%

Most occurring characters

ValueCountFrequency (%)
s 47678
15.9%
e 40000
13.3%
r 26161
8.7%
a 26161
8.7%
l 26161
8.7%
n 20000
6.7%
20000
6.7%
v 20000
6.7%
B 13839
 
4.6%
u 13839
 
4.6%
Other values (5) 46161
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 253839
84.6%
Uppercase Letter 26161
 
8.7%
Space Separator 20000
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 47678
18.8%
e 40000
15.8%
r 26161
10.3%
a 26161
10.3%
l 26161
10.3%
n 20000
7.9%
v 20000
7.9%
u 13839
 
5.5%
i 13839
 
5.5%
t 13839
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
B 13839
52.9%
P 6161
23.6%
T 6161
23.6%
Space Separator
ValueCountFrequency (%)
20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 280000
93.3%
Common 20000
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 47678
17.0%
e 40000
14.3%
r 26161
9.3%
a 26161
9.3%
l 26161
9.3%
n 20000
7.1%
v 20000
7.1%
B 13839
 
4.9%
u 13839
 
4.9%
i 13839
 
4.9%
Other values (4) 32322
11.5%
Common
ValueCountFrequency (%)
20000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 47678
15.9%
e 40000
13.3%
r 26161
8.7%
a 26161
8.7%
l 26161
8.7%
n 20000
6.7%
20000
6.7%
v 20000
6.7%
B 13839
 
4.6%
u 13839
 
4.6%
Other values (5) 46161
15.4%

Class
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
Business
9577 
Eco
8927 
Eco Plus
1496 

Length

Max length8
Median length8
Mean length5.76825
Min length3

Characters and Unicode

Total characters115365
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBusiness
2nd rowBusiness
3rd rowEco
4th rowEco
5th rowEco Plus

Common Values

ValueCountFrequency (%)
Business 9577
47.9%
Eco 8927
44.6%
Eco Plus 1496
 
7.5%

Length

2023-08-14T08:36:21.351703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:21.547680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
eco 10423
48.5%
business 9577
44.6%
plus 1496
 
7.0%

Most occurring characters

ValueCountFrequency (%)
s 30227
26.2%
u 11073
 
9.6%
E 10423
 
9.0%
c 10423
 
9.0%
o 10423
 
9.0%
B 9577
 
8.3%
i 9577
 
8.3%
n 9577
 
8.3%
e 9577
 
8.3%
1496
 
1.3%
Other values (2) 2992
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92373
80.1%
Uppercase Letter 21496
 
18.6%
Space Separator 1496
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 30227
32.7%
u 11073
 
12.0%
c 10423
 
11.3%
o 10423
 
11.3%
i 9577
 
10.4%
n 9577
 
10.4%
e 9577
 
10.4%
l 1496
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
E 10423
48.5%
B 9577
44.6%
P 1496
 
7.0%
Space Separator
ValueCountFrequency (%)
1496
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 113869
98.7%
Common 1496
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 30227
26.5%
u 11073
 
9.7%
E 10423
 
9.2%
c 10423
 
9.2%
o 10423
 
9.2%
B 9577
 
8.4%
i 9577
 
8.4%
n 9577
 
8.4%
e 9577
 
8.4%
P 1496
 
1.3%
Common
ValueCountFrequency (%)
1496
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115365
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 30227
26.2%
u 11073
 
9.6%
E 10423
 
9.0%
c 10423
 
9.0%
o 10423
 
9.0%
B 9577
 
8.3%
i 9577
 
8.3%
n 9577
 
8.3%
e 9577
 
8.3%
1496
 
1.3%
Other values (2) 2992
 
2.6%

Flight Distance
Real number (ℝ)

Distinct4268
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1987.6599
Minimum50
Maximum6951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:21.782828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile342
Q11369
median1929
Q32546.25
95-th percentile3848
Maximum6951
Range6901
Interquartile range (IQR)1177.25

Descriptive statistics

Standard deviation1025.247
Coefficient of variation (CV)0.51580602
Kurtosis0.28822753
Mean1987.6599
Median Absolute Deviation (MAD)590
Skewness0.45622494
Sum39753199
Variance1051131.3
MonotonicityNot monotonic
2023-08-14T08:36:22.092460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2215 21
 
0.1%
1639 19
 
0.1%
1742 19
 
0.1%
1972 18
 
0.1%
1571 18
 
0.1%
1809 17
 
0.1%
1499 17
 
0.1%
2010 17
 
0.1%
1975 17
 
0.1%
1747 16
 
0.1%
Other values (4258) 19821
99.1%
ValueCountFrequency (%)
50 3
< 0.1%
51 4
< 0.1%
52 3
< 0.1%
53 6
< 0.1%
55 2
 
< 0.1%
56 6
< 0.1%
57 6
< 0.1%
58 1
 
< 0.1%
59 2
 
< 0.1%
60 4
< 0.1%
ValueCountFrequency (%)
6951 1
< 0.1%
6763 1
< 0.1%
6664 1
< 0.1%
6595 1
< 0.1%
6078 1
< 0.1%
5984 1
< 0.1%
5970 1
< 0.1%
5924 1
< 0.1%
5835 1
< 0.1%
5816 1
< 0.1%

Seat comfort
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8316
Minimum0
Maximum5
Zeros750
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:22.326676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.394503
Coefficient of variation (CV)0.49247882
Kurtosis-0.94212586
Mean2.8316
Median Absolute Deviation (MAD)1
Skewness-0.084731974
Sum56632
Variance1.9446387
MonotonicityNot monotonic
2023-08-14T08:36:22.514360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 4515
22.6%
2 4436
22.2%
4 4308
21.5%
1 3243
16.2%
5 2748
13.7%
0 750
 
3.8%
ValueCountFrequency (%)
0 750
 
3.8%
1 3243
16.2%
2 4436
22.2%
3 4515
22.6%
4 4308
21.5%
5 2748
13.7%
ValueCountFrequency (%)
5 2748
13.7%
4 4308
21.5%
3 4515
22.6%
2 4436
22.2%
1 3243
16.2%
0 750
 
3.8%

Departure/Arrival time convenient
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9733
Minimum0
Maximum5
Zeros1020
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:22.710499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5277447
Coefficient of variation (CV)0.51382124
Kurtosis-1.10507
Mean2.9733
Median Absolute Deviation (MAD)1
Skewness-0.23113159
Sum59466
Variance2.3340038
MonotonicityNot monotonic
2023-08-14T08:36:22.920744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 4510
22.6%
5 4077
20.4%
2 3582
17.9%
3 3533
17.7%
1 3278
16.4%
0 1020
 
5.1%
ValueCountFrequency (%)
0 1020
 
5.1%
1 3278
16.4%
2 3582
17.9%
3 3533
17.7%
4 4510
22.6%
5 4077
20.4%
ValueCountFrequency (%)
5 4077
20.4%
4 4510
22.6%
3 3533
17.7%
2 3582
17.9%
1 3278
16.4%
0 1020
 
5.1%

Food and drink
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8402
Minimum0
Maximum5
Zeros928
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:23.120955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4457069
Coefficient of variation (CV)0.50901588
Kurtosis-0.9903876
Mean2.8402
Median Absolute Deviation (MAD)1
Skewness-0.1070745
Sum56804
Variance2.0900685
MonotonicityNot monotonic
2023-08-14T08:36:23.310495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 4359
21.8%
2 4168
20.8%
4 4130
20.6%
1 3301
16.5%
5 3114
15.6%
0 928
 
4.6%
ValueCountFrequency (%)
0 928
 
4.6%
1 3301
16.5%
2 4168
20.8%
3 4359
21.8%
4 4130
20.6%
5 3114
15.6%
ValueCountFrequency (%)
5 3114
15.6%
4 4130
20.6%
3 4359
21.8%
2 4168
20.8%
1 3301
16.5%
0 928
 
4.6%

Gate location
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.967
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:23.469950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3088914
Coefficient of variation (CV)0.44114978
Kurtosis-1.0933507
Mean2.967
Median Absolute Deviation (MAD)1
Skewness-0.025497211
Sum59340
Variance1.7131967
MonotonicityNot monotonic
2023-08-14T08:36:23.671264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 5206
26.0%
4 4463
22.3%
2 3836
19.2%
1 3568
17.8%
5 2926
14.6%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 3568
17.8%
2 3836
19.2%
3 5206
26.0%
4 4463
22.3%
5 2926
14.6%
ValueCountFrequency (%)
5 2926
14.6%
4 4463
22.3%
3 5206
26.0%
2 3836
19.2%
1 3568
17.8%
0 1
 
< 0.1%

Inflight wifi service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2566
Minimum0
Maximum5
Zeros18
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:23.852940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3216822
Coefficient of variation (CV)0.40584726
Kurtosis-1.1252496
Mean3.2566
Median Absolute Deviation (MAD)1
Skewness-0.19469914
Sum65132
Variance1.7468438
MonotonicityNot monotonic
2023-08-14T08:36:24.066143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 4795
24.0%
5 4525
22.6%
3 4271
21.4%
2 4123
20.6%
1 2268
11.3%
0 18
 
0.1%
ValueCountFrequency (%)
0 18
 
0.1%
1 2268
11.3%
2 4123
20.6%
3 4271
21.4%
4 4795
24.0%
5 4525
22.6%
ValueCountFrequency (%)
5 4525
22.6%
4 4795
24.0%
3 4271
21.4%
2 4123
20.6%
1 2268
11.3%
0 18
 
0.1%

Inflight entertainment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3868
Minimum0
Maximum5
Zeros438
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:24.240631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3433823
Coefficient of variation (CV)0.39665238
Kurtosis-0.54811763
Mean3.3868
Median Absolute Deviation (MAD)1
Skewness-0.59402164
Sum67736
Variance1.804676
MonotonicityNot monotonic
2023-08-14T08:36:24.490684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 6368
31.8%
5 4641
23.2%
3 3758
18.8%
2 2990
14.9%
1 1805
 
9.0%
0 438
 
2.2%
ValueCountFrequency (%)
0 438
 
2.2%
1 1805
 
9.0%
2 2990
14.9%
3 3758
18.8%
4 6368
31.8%
5 4641
23.2%
ValueCountFrequency (%)
5 4641
23.2%
4 6368
31.8%
3 3758
18.8%
2 2990
14.9%
1 1805
 
9.0%
0 438
 
2.2%

Online support
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
4
6283 
5
5570 
3
3319 
2
2666 
1
2162 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row5
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4 6283
31.4%
5 5570
27.9%
3 3319
16.6%
2 2666
13.3%
1 2162
 
10.8%

Length

2023-08-14T08:36:24.659105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:24.835295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 6283
31.4%
5 5570
27.9%
3 3319
16.6%
2 2666
13.3%
1 2162
 
10.8%

Most occurring characters

ValueCountFrequency (%)
4 6283
31.4%
5 5570
27.9%
3 3319
16.6%
2 2666
13.3%
1 2162
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6283
31.4%
5 5570
27.9%
3 3319
16.6%
2 2666
13.3%
1 2162
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6283
31.4%
5 5570
27.9%
3 3319
16.6%
2 2666
13.3%
1 2162
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6283
31.4%
5 5570
27.9%
3 3319
16.6%
2 2666
13.3%
1 2162
 
10.8%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
4
6051 
5
5324 
3
3475 
2
3118 
1
2032 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row4
4th row1
5th row4

Common Values

ValueCountFrequency (%)
4 6051
30.3%
5 5324
26.6%
3 3475
17.4%
2 3118
15.6%
1 2032
 
10.2%

Length

2023-08-14T08:36:25.014973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:25.183874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 6051
30.3%
5 5324
26.6%
3 3475
17.4%
2 3118
15.6%
1 2032
 
10.2%

Most occurring characters

ValueCountFrequency (%)
4 6051
30.3%
5 5324
26.6%
3 3475
17.4%
2 3118
15.6%
1 2032
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6051
30.3%
5 5324
26.6%
3 3475
17.4%
2 3118
15.6%
1 2032
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6051
30.3%
5 5324
26.6%
3 3475
17.4%
2 3118
15.6%
1 2032
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6051
30.3%
5 5324
26.6%
3 3475
17.4%
2 3118
15.6%
1 2032
 
10.2%

On-board service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
4
6121 
5
4944 
3
4181 
2
2719 
1
2035 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 6121
30.6%
5 4944
24.7%
3 4181
20.9%
2 2719
13.6%
1 2035
 
10.2%

Length

2023-08-14T08:36:25.545809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:25.727726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 6121
30.6%
5 4944
24.7%
3 4181
20.9%
2 2719
13.6%
1 2035
 
10.2%

Most occurring characters

ValueCountFrequency (%)
4 6121
30.6%
5 4944
24.7%
3 4181
20.9%
2 2719
13.6%
1 2035
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6121
30.6%
5 4944
24.7%
3 4181
20.9%
2 2719
13.6%
1 2035
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6121
30.6%
5 4944
24.7%
3 4181
20.9%
2 2719
13.6%
1 2035
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6121
30.6%
5 4944
24.7%
3 4181
20.9%
2 2719
13.6%
1 2035
 
10.2%

Leg room service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4802
Minimum0
Maximum5
Zeros73
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:25.916880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.2927844
Coefficient of variation (CV)0.37146842
Kurtosis-0.85902106
Mean3.4802
Median Absolute Deviation (MAD)1
Skewness-0.48154175
Sum69604
Variance1.6712915
MonotonicityNot monotonic
2023-08-14T08:36:26.102880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 6043
30.2%
5 5294
26.5%
3 3458
17.3%
2 3456
17.3%
1 1676
 
8.4%
0 73
 
0.4%
ValueCountFrequency (%)
0 73
 
0.4%
1 1676
 
8.4%
2 3456
17.3%
3 3458
17.3%
4 6043
30.2%
5 5294
26.5%
ValueCountFrequency (%)
5 5294
26.5%
4 6043
30.2%
3 3458
17.3%
2 3456
17.3%
1 1676
 
8.4%
0 73
 
0.4%

Baggage handling
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
4
7449 
5
5538 
3
3664 
2
2107 
1
1242 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row4
4th row1
5th row4

Common Values

ValueCountFrequency (%)
4 7449
37.2%
5 5538
27.7%
3 3664
18.3%
2 2107
 
10.5%
1 1242
 
6.2%

Length

2023-08-14T08:36:26.280426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:26.436349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 7449
37.2%
5 5538
27.7%
3 3664
18.3%
2 2107
 
10.5%
1 1242
 
6.2%

Most occurring characters

ValueCountFrequency (%)
4 7449
37.2%
5 5538
27.7%
3 3664
18.3%
2 2107
 
10.5%
1 1242
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7449
37.2%
5 5538
27.7%
3 3664
18.3%
2 2107
 
10.5%
1 1242
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 7449
37.2%
5 5538
27.7%
3 3664
18.3%
2 2107
 
10.5%
1 1242
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 7449
37.2%
5 5538
27.7%
3 3664
18.3%
2 2107
 
10.5%
1 1242
 
6.2%

Checkin service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
4
5689 
3
5405 
5
4201 
2
2403 
1
2302 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row5
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4 5689
28.4%
3 5405
27.0%
5 4201
21.0%
2 2403
12.0%
1 2302
11.5%

Length

2023-08-14T08:36:26.631039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:26.802040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 5689
28.4%
3 5405
27.0%
5 4201
21.0%
2 2403
12.0%
1 2302
11.5%

Most occurring characters

ValueCountFrequency (%)
4 5689
28.4%
3 5405
27.0%
5 4201
21.0%
2 2403
12.0%
1 2302
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 5689
28.4%
3 5405
27.0%
5 4201
21.0%
2 2403
12.0%
1 2302
11.5%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 5689
28.4%
3 5405
27.0%
5 4201
21.0%
2 2403
12.0%
1 2302
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 5689
28.4%
3 5405
27.0%
5 4201
21.0%
2 2403
12.0%
1 2302
11.5%

Cleanliness
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
4
7476 
5
5532 
3
3670 
2
2147 
1
1175 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row4
4th row5
5th row4

Common Values

ValueCountFrequency (%)
4 7476
37.4%
5 5532
27.7%
3 3670
18.4%
2 2147
 
10.7%
1 1175
 
5.9%

Length

2023-08-14T08:36:27.007836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:27.180455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 7476
37.4%
5 5532
27.7%
3 3670
18.4%
2 2147
 
10.7%
1 1175
 
5.9%

Most occurring characters

ValueCountFrequency (%)
4 7476
37.4%
5 5532
27.7%
3 3670
18.4%
2 2147
 
10.7%
1 1175
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7476
37.4%
5 5532
27.7%
3 3670
18.4%
2 2147
 
10.7%
1 1175
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 7476
37.4%
5 5532
27.7%
3 3670
18.4%
2 2147
 
10.7%
1 1175
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 7476
37.4%
5 5532
27.7%
3 3670
18.4%
2 2147
 
10.7%
1 1175
 
5.9%

Online boarding
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
4
5449 
3
4726 
5
4604 
2
2883 
1
2338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row5
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 5449
27.2%
3 4726
23.6%
5 4604
23.0%
2 2883
14.4%
1 2338
11.7%

Length

2023-08-14T08:36:27.420555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T08:36:27.577083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 5449
27.2%
3 4726
23.6%
5 4604
23.0%
2 2883
14.4%
1 2338
11.7%

Most occurring characters

ValueCountFrequency (%)
4 5449
27.2%
3 4726
23.6%
5 4604
23.0%
2 2883
14.4%
1 2338
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 5449
27.2%
3 4726
23.6%
5 4604
23.0%
2 2883
14.4%
1 2338
11.7%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 5449
27.2%
3 4726
23.6%
5 4604
23.0%
2 2883
14.4%
1 2338
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 5449
27.2%
3 4726
23.6%
5 4604
23.0%
2 2883
14.4%
1 2338
11.7%

Departure Delay in Minutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct290
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.3154
Minimum0
Maximum951
Zeros11422
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:27.772966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile76
Maximum951
Range951
Interquartile range (IQR)12

Descriptive statistics

Standard deviation35.931857
Coefficient of variation (CV)2.5100142
Kurtosis59.286883
Mean14.3154
Median Absolute Deviation (MAD)0
Skewness5.6934389
Sum286308
Variance1291.0984
MonotonicityNot monotonic
2023-08-14T08:36:27.986695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11422
57.1%
1 516
 
2.6%
2 453
 
2.3%
3 367
 
1.8%
4 363
 
1.8%
5 329
 
1.6%
8 256
 
1.3%
6 256
 
1.3%
7 253
 
1.3%
10 236
 
1.2%
Other values (280) 5549
27.7%
ValueCountFrequency (%)
0 11422
57.1%
1 516
 
2.6%
2 453
 
2.3%
3 367
 
1.8%
4 363
 
1.8%
5 329
 
1.6%
6 256
 
1.3%
7 253
 
1.3%
8 256
 
1.3%
9 235
 
1.2%
ValueCountFrequency (%)
951 1
< 0.1%
624 1
< 0.1%
610 1
< 0.1%
501 1
< 0.1%
452 1
< 0.1%
447 1
< 0.1%
423 1
< 0.1%
419 1
< 0.1%
414 1
< 0.1%
407 1
< 0.1%

Arrival Delay in Minutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct292
Distinct (%)1.5%
Missing48
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean14.755513
Minimum0
Maximum940
Zeros11224
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-08-14T08:36:28.186109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile78
Maximum940
Range940
Interquartile range (IQR)13

Descriptive statistics

Standard deviation36.413099
Coefficient of variation (CV)2.4677623
Kurtosis55.711879
Mean14.755513
Median Absolute Deviation (MAD)0
Skewness5.5628445
Sum294402
Variance1325.9138
MonotonicityNot monotonic
2023-08-14T08:36:28.393865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11224
56.1%
1 422
 
2.1%
2 412
 
2.1%
4 380
 
1.9%
3 367
 
1.8%
5 308
 
1.5%
7 307
 
1.5%
6 306
 
1.5%
8 280
 
1.4%
10 238
 
1.2%
Other values (282) 5708
28.5%
ValueCountFrequency (%)
0 11224
56.1%
1 422
 
2.1%
2 412
 
2.1%
3 367
 
1.8%
4 380
 
1.9%
5 308
 
1.5%
6 306
 
1.5%
7 307
 
1.5%
8 280
 
1.4%
9 218
 
1.1%
ValueCountFrequency (%)
940 1
< 0.1%
615 1
< 0.1%
593 1
< 0.1%
500 1
< 0.1%
460 1
< 0.1%
444 1
< 0.1%
438 1
< 0.1%
436 1
< 0.1%
432 1
< 0.1%
412 1
< 0.1%

Interactions

2023-08-14T08:36:15.961532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:52.683405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:54.946161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:57.030679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:59.267276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:01.439644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:03.529240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:05.637685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:07.813032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:10.117708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:12.165823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:14.441385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:16.104087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:52.895302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:55.131293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:57.253793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:59.426733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:01.601273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:03.781109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:05.815135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:07.987963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:10.284785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:12.400195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:14.599010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:16.248354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:53.040008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:55.336318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:57.422480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:59.605611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:01.772965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:04.050710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:05.994225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:08.158954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:10.435301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:12.658018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:14.749896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:16.386878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:53.181848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:55.498452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:57.588764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:59.803269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:01.940956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:04.213326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:06.192022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:08.332891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:10.683045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:12.884503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:14.881499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:16.506496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:53.378756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:55.676904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:57.738459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:00.121200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:02.135575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:04.368593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:06.348557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:08.553001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:10.856576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:13.080477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:15.000574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:16.625649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:53.566325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:55.828370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:57.900801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:00.253415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:02.284953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:04.532486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:06.524552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:08.735716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:11.013001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:13.251864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:15.121115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:16.756966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:53.766656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:55.981687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:58.073203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:00.400064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:02.440246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:04.688875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:06.706561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:08.879738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:11.174076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:13.452720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:15.238311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:16.902384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:53.939759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:56.143481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:58.286446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:00.550165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:02.603498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:04.845768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:06.905188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:09.223179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:11.313625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:13.604513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:15.361239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:17.154432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:54.151871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:56.301525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:58.525385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:00.723141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:02.780950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:05.000672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:07.104956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:09.463716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:11.483891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:13.736310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:15.479616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:17.404563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:54.425799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:56.474982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:58.737683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:00.889836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:02.935096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:05.162860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:07.302257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:09.635909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:11.686962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:13.916186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:15.598658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:17.546784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:54.623678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:56.675153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:58.932635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:01.112813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:03.107278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:05.347032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:07.465524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:09.790043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:11.834080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:14.113379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:15.716871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:17.809326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:54.786710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:56.853249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:35:59.116502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:01.291477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:03.279566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:05.497792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:07.661352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:09.958774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:11.973244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:14.291119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-14T08:36:15.839995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-14T08:36:28.558674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idAgeFlight DistanceSeat comfortDeparture/Arrival time convenientFood and drinkGate locationInflight wifi serviceInflight entertainmentLeg room serviceDeparture Delay in MinutesArrival Delay in Minutessatisfaction_v2GenderCustomer TypeType of TravelClassOnline supportEase of Online bookingOn-board serviceBaggage handlingCheckin serviceCleanlinessOnline boarding
id1.0000.0300.063-0.0280.0010.0060.004-0.0040.0520.0370.0640.0080.0200.0000.0050.0110.1270.0440.0000.0480.0680.0790.0680.027
Age0.0301.000-0.2400.0010.0390.008-0.0040.0090.1200.094-0.001-0.0080.1860.0220.3820.3530.2080.0870.0580.0670.0470.0420.0440.049
Flight Distance0.063-0.2401.000-0.050-0.002-0.018-0.0000.001-0.033-0.0240.0550.0350.1840.1690.2640.1550.2120.0620.0460.0470.0350.0160.0350.040
Seat comfort-0.0280.001-0.0501.0000.4390.7030.3960.1300.3860.118-0.023-0.0270.4690.1190.0790.0760.0630.1270.2100.1080.1290.0490.1120.125
Departure/Arrival time convenient0.0010.039-0.0020.4391.0000.5450.550-0.0130.0570.0140.0100.0010.0390.0640.2730.2160.0680.0340.0420.0480.0600.0530.0640.022
Food and drink0.0060.008-0.0180.7030.5451.0000.5260.0130.3120.059-0.006-0.0090.2660.0860.0900.0880.0640.0230.0370.0250.0380.0270.0300.016
Gate location0.004-0.004-0.0000.3960.5500.5261.000-0.015-0.017-0.0130.0230.0150.1460.0450.1430.0820.0880.0350.0380.0440.0540.0400.0540.025
Inflight wifi service-0.0040.0090.0010.130-0.0130.013-0.0151.0000.2660.041-0.010-0.0160.2430.0370.1030.0350.0580.4980.5690.0400.0410.0560.0430.579
Inflight entertainment0.0520.120-0.0330.3860.0570.312-0.0170.2661.0000.177-0.038-0.0520.6430.1530.2590.0720.1730.3060.2280.1180.1020.1420.0970.255
Leg room service0.0370.094-0.0240.1180.0140.059-0.0130.0410.1771.000-0.005-0.0110.3340.0990.1280.0820.1050.0930.3120.3320.3170.0990.3210.073
Departure Delay in Minutes0.064-0.0010.055-0.0230.010-0.0060.023-0.010-0.038-0.0051.0000.7410.0430.0130.0000.0060.0000.0200.0160.0210.0130.0240.0310.017
Arrival Delay in Minutes0.008-0.0080.035-0.0270.001-0.0090.015-0.016-0.052-0.0110.7411.0000.0400.0100.0000.0100.0000.0190.0110.0180.0110.0260.0300.018
satisfaction_v20.0200.1860.1840.4690.0390.2660.1460.2430.6430.3340.0430.0401.0000.2110.2950.1070.3080.4250.4500.3610.3080.2770.3010.339
Gender0.0000.0220.1690.1190.0640.0860.0450.0370.1530.0990.0130.0100.2111.0000.0270.0000.0210.0960.0820.0590.0350.0200.0340.061
Customer Type0.0050.3820.2640.0790.2730.0900.1430.1030.2590.1280.0000.0000.2950.0271.0000.3110.1140.2030.1650.1080.0670.0500.0610.132
Type of Travel0.0110.3530.1550.0760.2160.0880.0820.0350.0720.0820.0060.0100.1070.0000.3111.0000.5590.0660.0350.0550.0700.0730.0800.026
Class0.1270.2080.2120.0630.0680.0640.0880.0580.1730.1050.0000.0000.3080.0210.1140.5591.0000.1380.0990.1230.1030.1080.0990.082
Online support0.0440.0870.0620.1270.0340.0230.0350.4980.3060.0930.0200.0190.4250.0960.2030.0660.1381.0000.5290.1010.0900.1260.0870.557
Ease of Online booking0.0000.0580.0460.2100.0420.0370.0380.5690.2280.3120.0160.0110.4500.0820.1650.0350.0990.5291.0000.4040.3740.0750.4100.594
On-board service0.0480.0670.0470.1080.0480.0250.0440.0400.1180.3320.0210.0180.3610.0590.1080.0550.1230.1010.4041.0000.4200.1350.4450.076
Baggage handling0.0680.0470.0350.1290.0600.0380.0540.0410.1020.3170.0130.0110.3080.0350.0670.0700.1030.0900.3740.4201.0000.1350.5020.068
Checkin service0.0790.0420.0160.0490.0530.0270.0400.0560.1420.0990.0240.0260.2770.0200.0500.0730.1080.1260.0750.1350.1351.0000.1430.108
Cleanliness0.0680.0440.0350.1120.0640.0300.0540.0430.0970.3210.0310.0300.3010.0340.0610.0800.0990.0870.4100.4450.5020.1431.0000.065
Online boarding0.0270.0490.0400.1250.0220.0160.0250.5790.2550.0730.0170.0180.3390.0610.1320.0260.0820.5570.5940.0760.0680.1080.0651.000

Missing values

2023-08-14T08:36:18.041956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-14T08:36:18.490816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idsatisfaction_v2GenderCustomer TypeAgeType of TravelClassFlight DistanceSeat comfortDeparture/Arrival time convenientFood and drinkGate locationInflight wifi serviceInflight entertainmentOnline supportEase of Online bookingOn-board serviceLeg room serviceBaggage handlingCheckin serviceCleanlinessOnline boardingDeparture Delay in MinutesArrival Delay in Minutes
10288660672satisfiedFemaleLoyal Customer40Business travelBusiness16223333555444434300.0
6984959483neutral or dissatisfiedFemaleLoyal Customer50Business travelBusiness20321555433112121250.0
1639528722neutral or dissatisfiedMaleLoyal Customer40Personal TravelEco4256252125543545453210.0
9193936785satisfiedFemaleLoyal Customer38Business travelEco25914414444132145490.0
332325015neutral or dissatisfiedFemaleLoyal Customer49Personal TravelEco Plus52042443334444344015.0
872270906satisfiedFemaleLoyal Customer11Personal TravelEco27461511244444454500.0
10361972827satisfiedMaleLoyal Customer44Business travelBusiness18493333554444434400.0
10354468436satisfiedFemaleLoyal Customer38Business travelBusiness10535555445444444300.0
4259276074neutral or dissatisfiedMaledisloyal Customer34Business travelEco24731113515533433500.0
107830104220satisfiedFemaleLoyal Customer16Business travelBusiness30414444444434454472.0
idsatisfaction_v2GenderCustomer TypeAgeType of TravelClassFlight DistanceSeat comfortDeparture/Arrival time convenientFood and drinkGate locationInflight wifi serviceInflight entertainmentOnline supportEase of Online bookingOn-board serviceLeg room serviceBaggage handlingCheckin serviceCleanlinessOnline boardingDeparture Delay in MinutesArrival Delay in Minutes
2530621319satisfiedFemaleLoyal Customer64Personal TravelEco Plus7012222255555535300.0
302511177satisfiedFemaleLoyal Customer61Personal TravelEco3475551151135434100.0
40223117519satisfiedMaledisloyal Customer22Business travelEco25440003204253455200.0
12961422256satisfiedFemaleLoyal Customer39Business travelBusiness23422224425555251223.0
125711120899satisfiedMaleLoyal Customer48Business travelBusiness40314444355555555400.0
12506760358satisfiedMaleLoyal Customer45Business travelBusiness14615455555555555370.0
5120750464neutral or dissatisfiedMaledisloyal Customer80Business travelEco19383044343325442300.0
7661126617satisfiedFemaleLoyal Customer47Personal TravelEco Plus13411111343352434300.0
12503092041satisfiedMaleLoyal Customer40Business travelBusiness15622222445555545500.0
7864641859neutral or dissatisfiedFemaleLoyal Customer23Business travelEco22143111331312343300.0